Gas well productivity prediction method and device based on flow factor classification

Abstract

The invention discloses a gas well productivity prediction method and device based on flow factor classification, and the method comprises the steps: determining a first productivity factor of a reservoir according to a gas well production parameter, and determining a second productivity factor of the reservoir according to a gas well pressure parameter; determining a flow factor according to the formation coefficient, the fluid viscosity and the reservoir pressure, and determining a classification coefficient corresponding to the flow factor according to a preset classification reference; constructing a productivity prediction model according to the classification coefficient, the first reservoir productivity factor and the second reservoir productivity factor; and predicting the productivity of the gas well according to the productivity prediction model. The flow factor is determined, classification is performed according to the flow factor, and the productivity prediction model is constructed based on the classification coefficient of the flow factor and in combination with the first productivity factor and the second productivity factor of the reservoir, so that the obtained actual productivity is basically consistent with the gas well productivity obtained by the productivity prediction model, and the accuracy of predicting the gas well productivity is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of oil and gas field development, and in particular to a method and device for predicting gas well productivity based on flow factor classification. Background Art

[0002] In the field of oil and gas field development, gas well productivity prediction is a very important link in gas field exploration and development, providing a direct reference for development decisions. However, gas well productivity prediction is often restricted by various reservoir factors and cannot meet application requirements.

[0003] Existing technologies for predicting gas well productivity primarily utilize methods such as formula calculation, numerical simulation, and empirical models. However, formula calculation methods are affected by difficult-to-determine parameters such as the skin factor, resulting in low accuracy. Numerical simulation methods are complex and time-consuming. Empirical models are also affected by changes in well conditions or production schedules, resulting in low accuracy. These methods all suffer from low prediction accuracy, and none of them consider the ability to accurately predict gas well productivity by classifying different formation parameters. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a method and device for predicting gas well productivity based on flow factor classification, which overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of an embodiment of the present invention, a method for predicting gas well productivity based on flow factor classification is provided, the method comprising:

[0006] determining a first reservoir productivity factor based on the gas well production parameters, and determining a second reservoir productivity factor based on the gas well pressure parameters;

[0007] Determine the flow factor according to the formation coefficient, fluid viscosity and reservoir pressure, and determine the classification coefficient corresponding to the flow factor according to the preset classification benchmark;

[0008] Constructing a productivity prediction model based on the classification coefficient, the first productivity factor of the reservoir and the second productivity factor of the reservoir;

[0009] The gas well productivity is predicted based on the productivity prediction model.

[0010] According to another aspect of an embodiment of the present invention, a gas well productivity prediction device based on flow factor classification is provided, comprising:

[0011] A factor determination module, adapted to determine a first reservoir productivity factor according to a gas well production parameter, and to determine a second reservoir productivity factor according to a gas well pressure parameter;

[0012] A flow factor classification module is adapted to determine the flow factor according to the formation coefficient, fluid viscosity and reservoir pressure, and to determine the classification coefficient corresponding to the flow factor according to a preset classification benchmark;

[0013] A model building module, adapted to build a productivity prediction model based on the classification coefficient, the first productivity factor of the reservoir, and the second productivity factor of the reservoir;

[0014] The prediction module is suitable for predicting the gas well productivity based on the productivity prediction model.

[0015] According to another aspect of an embodiment of the present invention, there is provided a computing device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0016] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned gas well productivity prediction method based on flow factor classification.

[0017] According to another aspect of an embodiment of the present invention, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the above-mentioned gas well productivity prediction method based on flow factor classification.

[0018] According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the above-mentioned gas well productivity prediction method based on flow factor classification.

[0019] According to the gas well productivity prediction method and device based on flow factor classification provided by the embodiments of the present invention, the flow factor is determined, classification is performed according to the flow factor, and a productivity prediction model is constructed based on the classification coefficient of the flow factor and combined with the first reservoir productivity factor and the second reservoir productivity factor. The actual productivity obtained is basically consistent with the gas well productivity obtained by the productivity prediction model, thereby improving the accuracy of predicting the gas well productivity.

[0020] The above description is only an overview of the technical solutions of the embodiments of the present invention. In order to more clearly understand the technical means of the embodiments of the present invention, they can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiments of the present invention more obvious and easy to understand, the specific implementation methods of the embodiments of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the embodiments of the present invention. The same reference numerals are used throughout the accompanying drawings to denote the same components. In the accompanying drawings:

[0022] Figure 1 A flow chart of a method for predicting gas well productivity based on flow factor classification according to an embodiment of the present invention is shown;

[0023] Figure 2 A schematic diagram showing the relationship between the ratio of gas well productivity to the first reservoir productivity factor and the second reservoir productivity factor in the unclassified case is shown;

[0024] Figure 3 A schematic diagram showing the relationship between the ratio of gas well productivity to the first productivity factor of the reservoir and the second productivity factor of the reservoir under classification conditions is shown;

[0025] Figure 4 A schematic structural diagram of a gas well productivity prediction device based on flow factor classification according to an embodiment of the present invention is shown;

[0026] Figure 5 A schematic structural diagram of a computing device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0027] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0028] Figure 1 FIG. 1 shows a flow chart of a method for predicting gas well productivity based on flow factor classification according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0029] Step S101 : determining a first reservoir productivity factor according to a gas well production parameter, and determining a second reservoir productivity factor according to a gas well pressure parameter.

[0030] The productivity of a gas well is affected by the production parameters and pressure parameters of the gas well. In this embodiment, factors related to the productivity of the gas well are obtained based on the production parameters and pressure parameters of the gas well.

[0031] Specifically, the reservoir's primary productivity factor can be determined based on the formation coefficient, reservoir porosity, gas saturation, and reservoir pressure. The reservoir's primary productivity factor is directly proportional to the formation coefficient, reservoir porosity, gas saturation, and reservoir pressure. For example, the reservoir's primary productivity factor is the product of the formation coefficient, reservoir porosity, gas saturation, and reservoir pressure. The formation coefficient characterizes the formation's gas supply capacity and reflects the formation's flow characteristics; reservoir porosity characterizes the reservoir's storage capacity; gas saturation characterizes the reservoir's gas content; and reservoir pressure characterizes the formation's elastic energy; the greater the reservoir pressure, the greater the productivity potential. The reservoir's primary productivity factor, derived from the above gas well production parameters, is used to characterize gas formation productivity information.

[0032] The second productivity factor of the reservoir can be determined based on the bottom hole flow pressure and the reservoir pressure. The bottom hole flow pressure can characterize the energy level of the formation. The higher the flow pressure, the more sufficient the formation energy is, and the more favorable it is for oil and gas development. The second productivity factor of the reservoir obtained from the above gas well pressure parameters is used to characterize the gas well flow pressure information. The second productivity factor of the reservoir is inversely proportional to the ratio of the bottom hole flow pressure and the reservoir pressure. The square of the ratio of the bottom hole flow pressure and the reservoir pressure calculated in advance can be used to determine the value range of the second productivity factor of the reservoir. For example, the square of the ratio of the bottom hole flow pressure and the reservoir pressure can be subtracted from the preset range threshold to obtain the second productivity factor of the reservoir. The above is an example, and the specific setting is based on the implementation situation and is not limited here.

[0033] Step S102: determining a flow factor according to the formation coefficient, fluid viscosity, and reservoir pressure, and determining a classification coefficient corresponding to the flow factor according to a preset classification benchmark.

[0034] According to the determined reservoir first productivity factor and reservoir second productivity factor, it can be determined that the gas well productivity is related to the reservoir first productivity factor and reservoir second productivity factor, such as Figure 2 As shown, the horizontal axis is the second productivity factor of the reservoir, and the vertical axis is the ratio of the gas well productivity to the first productivity factor of the reservoir. With the horizontal axis as x and the vertical axis as y, we can get y=2664.6x 1.2948 Here, the power exponent can improve the correlation of the data and obtain the fitting accuracy R 2 =0.8398. Further based on Figure 2 In the dotted line, you can find Figure 2 The data represented by the squares in the middle have a classification trend indicated by the dotted line (regression curve). Therefore, the ratio of gas well productivity to the first productivity factor of the reservoir and the second productivity factor of the reservoir are further refined to obtain Figure 3 The results are shown. Figure 3 As shown in the figure, the horizontal axis is the second productivity factor of the reservoir, and the vertical axis is the ratio of the gas well productivity to the first productivity factor of the reservoir. Figure 3 The data can be divided into different categories, and the classification is determined by different values ​​of the flow factor.

[0035] Specifically, the flow factor can be determined based on the formation coefficient, fluid viscosity, and reservoir pressure. For example, the product of fluid viscosity and reservoir pressure is first calculated, followed by the ratio of the formation coefficient to this product. This ratio is then logarithmically processed to yield the flow factor. Specifically, the flow factor η = ln(formation coefficient / (fluid viscosity × reservoir pressure)). Here, the formation coefficient and fluid viscosity, which reflect the flow characteristics of the formation, combined with the reservoir pressure, which reflects the energy level of the formation, further reflect the formation's ability to release energy to supply fluid flow, thereby determining different classifications. Given that the ratio of the formation coefficient to the product is often large, logarithmic processing can reduce the absolute value, facilitating classification.

[0036] After obtaining the flow factor, compare the flow factor with different classification thresholds of the preset classification benchmark to determine the corresponding classification. For example, if the preset classification benchmark is three categories, the flow factor η corresponding to category one is greater than 5.5, the flow factor 5.5≥η>4.2 corresponding to category two, and the flow factor η≤4.2 corresponding to category three. Figure 3 As shown on the left, class one is represented by blue, class two is represented by purple, and class three is represented by red.

[0037] Determine the classification coefficient corresponding to the flow factor based on the classification. The classification coefficient includes the first classification coefficient and the second classification coefficient. For example, the first classification coefficient for Class 1 is 582.13, and the second classification coefficient is 0.8841; the first classification coefficient for Class 2 is 1300, and the second classification coefficient is 0.8603; and the first classification coefficient for Class 3 is 4469.3, and the second classification coefficient is 1.0772. The above data are for illustration only and can be set according to implementation conditions, and are not limited here.

[0038] Step S103: constructing a productivity prediction model based on the classification coefficient, the first reservoir productivity factor, and the second reservoir productivity factor.

[0039] According to the determined classification coefficient, when constructing the capacity prediction model, the second capacity factor of the reservoir can be used as the base number and the second classification coefficient of the classification coefficient can be used as the index to obtain the first result, and the capacity prediction model can be constructed based on the first classification coefficient of the classification coefficient, the first capacity factor of the reservoir and the first result. For example, the capacity prediction model = the first capacity factor of the reservoir × the first classification coefficient × the second classification coefficient of the second capacity factor of the reservoir. Figure 3 As shown in , the horizontal axis is the second productivity factor of the reservoir, and the vertical axis is the ratio of the gas well productivity to the first productivity factor of the reservoir. With the horizontal axis as x and the vertical axis as y, for one type, we can get y = 582.13x 0.8841 ; For the second category, we can get y = 1300x 0.8603 ; For three categories, we can get y = 4469.3x 1.0772By calculating the fitting accuracy of the above three types of capacity forecast models, we can get the fitting accuracy R of the first type. 2 =0.9662, the fitting accuracy of the second category R 2 =0.9281, the fitting accuracy of the three categories R 2 =0.9592, which is much higher than the fitting accuracy in the unclassified case.

[0040] Furthermore, for the productivity prediction model, the productivity of new gas wells tested by DST (Drill Stem Testing, mid-way testing of gas wells) can also be calculated and compared with the traditional analytical method and DST test results. The basic reservoir parameters of the test section of the well are obtained, such as the perforation layer section of 4289.5-4313.3m, effective thickness of 20.4m, porosity of 13.6%, permeability of 11.4mD, temperature of 136°C, fluid viscosity of 0.0526cP, compression factor of 1.27, gas saturation of 50.24%, reservoir pressure of 50.9Mpa, etc. According to the above basic reservoir parameters, the formation coefficient, bottom hole flow pressure, etc. can be further obtained. The flow factor is first calculated to be 4.46, which is the second category. Then, based on the classification coefficient of the second category, it is substituted into the productivity prediction model to obtain the gas well productivity. As shown in Table 1 below, the productivity prediction model using the traditional analytical method, classification under different production systems of the well, Figure 2 The unclassified productivity model is used to calculate the productivity of the gas well.

[0041] Table 1

[0042]

[0043] Table 1 shows that the traditional analytical method yields relatively low accuracy for this gas well. While the results obtained using the unclassified productivity model are better than those obtained using the traditional analytical method, they still exhibit a certain degree of error. The productivity model, after flow factor classification, also provides relatively accurate results for the gas well's productivity. The average relative error for the productivity obtained under three different gas nozzles (i.e., different pressure differentials) is 24.83%. Error analysis results indicate that the actual productivity closely matches the model-calculated gas well productivity, achieving a relatively accurate prediction of the gas well's productivity.

[0044] Step S104: predicting the gas well productivity based on the productivity prediction model.

[0045] The capacity prediction model can be applied to various gas wells, obtaining corresponding gas well production parameters and gas well pressure parameters. Based on formation coefficients, fluid viscosity, formation pressure, and other parameters, these parameters are substituted into the capacity prediction model to predict gas well capacity. As shown in Table 2 below, the capacity prediction model is applied to each well in Table 2. Classifications based on the calculated flow factors are used, and the capacity prediction model is used to predict gas well capacity based on these classifications, resulting in highly accurate prediction results.

[0046] Table 2

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] According to the gas well productivity prediction method based on flow factor classification provided by an embodiment of the present invention, the flow factor is determined, classification is performed according to the flow factor, and a productivity prediction model is constructed based on the classification coefficient of the flow factor and combined with the first reservoir productivity factor and the second reservoir productivity factor. The actual productivity obtained is basically consistent with the gas well productivity obtained by the productivity prediction model, thereby improving the accuracy of predicting the gas well productivity.

[0053] Figure 4 FIG. 1 shows a schematic diagram of the structure of a gas well productivity prediction device based on flow factor classification provided by an embodiment of the present invention. Figure 4 As shown, the device includes:

[0054] The factor determination module 410 is adapted to determine a first reservoir productivity factor according to the gas well production parameter, and to determine a second reservoir productivity factor according to the gas well pressure parameter;

[0055] The flow factor classification module 420 is adapted to determine the flow factor according to the formation coefficient, fluid viscosity and reservoir pressure, and determine the classification coefficient corresponding to the flow factor according to a preset classification benchmark;

[0056] A model building module 430 is adapted to build a productivity prediction model based on the classification coefficient, the first reservoir productivity factor, and the second reservoir productivity factor;

[0057] The prediction module 440 is adapted to predict the gas well productivity according to the productivity prediction model.

[0058] Optionally, the factor determination module 410 is further adapted to:

[0059] The first reservoir productivity factor is determined based on the formation coefficient, reservoir porosity, gas saturation and reservoir pressure. The first reservoir productivity factor is proportional to the formation coefficient, reservoir porosity, gas saturation and reservoir pressure. The first reservoir productivity factor is used to characterize gas reservoir productivity information.

[0060] Optionally, the factor determination module 410 is further adapted to:

[0061] The second reservoir productivity factor is determined based on the bottom hole flowing pressure and the reservoir pressure. The second reservoir productivity factor is inversely proportional to the ratio of the bottom hole flowing pressure to the reservoir pressure. The second reservoir productivity factor is used to characterize the gas well flowing pressure information.

[0062] Optionally, the flow factor classification module 420 is further adapted to:

[0063] The product of fluid viscosity and reservoir pressure is calculated;

[0064] Calculate the ratio of the formation coefficient to the product, take the logarithm of the comparison value, and obtain the flow factor.

[0065] Optionally, the flow factor classification module 420 is further adapted to:

[0066] The flow factor is compared with different classification thresholds of the preset classification benchmark to determine the corresponding classification, and the classification coefficient corresponding to the flow factor is determined according to the classification; the classification coefficient includes a first classification coefficient and a second classification coefficient.

[0067] Optionally, the model building module 430 is further adapted to:

[0068] A first result is obtained with the second productivity factor of the reservoir as the base and the second classification coefficient of the classification coefficient as the exponent, and a productivity prediction model is constructed according to the first classification coefficient of the classification coefficient, the first productivity factor of the reservoir and the first result.

[0069] The description of each module above refers to the corresponding description in the method embodiment and will not be repeated here.

[0070] An embodiment of the present invention further provides a non-volatile computer storage medium storing at least one executable instruction, which can execute operations corresponding to the gas well productivity prediction method based on flow factor classification in any of the above method embodiments.

[0071] An embodiment of the present application provides a computer program product, which includes at least one executable instruction or computer program, which enables a processor to perform operations corresponding to the gas well productivity prediction method based on flow factor classification in any of the above method embodiments.

[0072] Figure 5 A schematic structural diagram of a computing device according to an embodiment of the present invention is shown. The specific implementation of the computing device is not limited to the specific implementation of the computing device in the specific embodiment of the present invention.

[0073] like Figure 5 As shown, the computing device may include a processor 502 , a communication interface 504 , a memory 506 , and a communication bus 508 .

[0074] in:

[0075] The processor 502 , the communication interface 504 , and the memory 506 communicate with each other via a communication bus 508 .

[0076] The communication interface 504 is used to communicate with other devices such as clients or other servers.

[0077] The processor 502 is configured to execute the program 510 , and specifically to execute the relevant steps in the embodiment of the gas well productivity prediction method based on flow factor classification.

[0078] Specifically, the program 510 may include program codes, which include computer operation instructions.

[0079] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in a computing device may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0080] The memory 506 is used to store the program 510. The memory 506 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0081] Program 510 can specifically be used to cause processor 502 to execute the gas well productivity prediction method based on flow factor classification described in any of the above-described method embodiments. The specific implementation of each step in program 510 can be found in the corresponding descriptions of the corresponding steps and units in the above-described gas well productivity prediction method based on flow factor classification, and will not be repeated here. Those skilled in the art will clearly understand that, for ease and brevity of description, the specific operating processes of the devices and modules described above can refer to the corresponding process descriptions in the above-described method embodiments, and will not be repeated here.

[0082] The algorithm or display provided herein is not inherently related to any particular computer, virtual system or other device. Various general-purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the embodiment of the present invention is not directed to any specific programming language. It should be understood that various programming languages ​​can be utilized to implement the content of the embodiment of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the preferred implementation of the embodiment of the present invention.

[0083] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0084] Similarly, it should be understood that in order to streamline the embodiments of the invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed approach should not be interpreted as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as reflected in the claims below, inventive aspects lie in less than all of the features of the individual embodiments disclosed above. Accordingly, the claims that follow the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

[0085] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0086] Furthermore, those skilled in the art will appreciate that although some embodiments herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in the claims below, any of the claimed embodiments may be used in any combination.

[0087] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The embodiments of the present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing an embodiment of the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0088] It should be noted that the above embodiments illustrate rather than limit the embodiments of the invention, and that a person skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The embodiments of the invention may be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments should not be understood as limiting the order of execution unless otherwise specified.

Claims

1. A gas well productivity prediction method based on flow factor classification, characterized in that the method include: determining a first reservoir productivity factor based on the gas well production parameters, and determining a second reservoir productivity factor based on the gas well pressure parameters; Determining a flow factor according to a formation coefficient, fluid viscosity, and reservoir pressure, and determining a classification coefficient corresponding to the flow factor according to a preset classification benchmark; Constructing a productivity prediction model based on the classification coefficient, the first reservoir productivity factor, and the second reservoir productivity factor; The gas well productivity is predicted according to the productivity prediction model.

2. The method according to claim 1, characterized in that Determining the first reservoir productivity factor according to the gas well production parameters further includes: The first reservoir productivity factor is determined based on the formation coefficient, reservoir porosity, gas saturation and reservoir pressure; the first reservoir productivity factor is proportional to the formation coefficient, reservoir porosity, gas saturation and reservoir pressure; the first reservoir productivity factor is used to characterize gas layer productivity information.

3. The method according to claim 1, characterized in that Determining the second reservoir productivity factor according to the gas well pressure parameter further includes: The second reservoir productivity factor is determined according to the bottom hole flow pressure and the reservoir pressure; the second reservoir productivity factor is inversely proportional to the ratio of the bottom hole flow pressure to the reservoir pressure; the second reservoir productivity factor is used to characterize the gas well flow pressure information.

4. The method according to claim 1, wherein Determining the flow factor according to the formation coefficient, fluid viscosity and reservoir pressure further includes: The product of fluid viscosity and reservoir pressure is calculated; The ratio of the formation coefficient to the product is calculated, and the ratio is logarithmically processed to obtain the flow factor.

5. The method according to claim 1, wherein Determining the classification coefficient corresponding to the flow factor according to the preset classification benchmark further includes: The flow factor is compared with different classification thresholds of a preset classification benchmark to determine the corresponding classification, and the classification coefficient corresponding to the flow factor is determined based on the classification; the classification coefficient includes a first classification coefficient and a second classification coefficient.

6. The method according to claim 4, characterized in that The constructing of the productivity prediction model according to the classification coefficient, the first reservoir productivity factor and the second reservoir productivity factor further comprises: A first result is obtained with the second productivity factor of the reservoir as a base and the second classification coefficient of the classification coefficient as an exponent, and a productivity prediction model is constructed based on the first classification coefficient of the classification coefficient, the first productivity factor of the reservoir and the first result.

7. A gas well productivity prediction device based on flow factor classification, characterized in that: The device includes: A factor determination module, adapted to determine a first reservoir productivity factor according to a gas well production parameter, and to determine a second reservoir productivity factor according to a gas well pressure parameter; A flow factor classification module, adapted to determine a flow factor according to a formation coefficient, fluid viscosity, and reservoir pressure, and to determine a classification coefficient corresponding to the flow factor according to a preset classification benchmark; A model building module, adapted to build a productivity prediction model based on the classification coefficient, the first reservoir productivity factor, and the second reservoir productivity factor; The prediction module is adapted to predict the gas well productivity according to the productivity prediction model.

8. A computing device, characterized in that include: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the gas well productivity prediction method based on flow factor classification according to any one of claims 1 to 6.

9. A computer storage medium, characterized in that The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the gas well productivity prediction method based on flow factor classification according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the gas well productivity prediction method based on flow factor classification according to any one of claims 1 to 6.

Images